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data_visualization.py
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data_visualization.py
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import matplotlib
matplotlib.use('Agg')
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import os.path as osp
def plot_clusters_vs_lambda(X_org,l,filename,dataset, lmbda, fairness_error):
K = max(l) +1
COLORSX = np.array(['rD','gP'])
plt.figure(1,figsize=(6.4,4.8))
plt.ion()
plt.clf()
group = ['cluster 1', 'cluster 2']
for k in range(K):
idx = np.asarray(np.where(l == k)).squeeze()
plt.plot(X_org[idx,0],X_org[idx,1],COLORSX[k],label = group[k]);
if dataset == 'Synthetic':
tmp_title = '$\lambda$ = {}, fairness Error = {: .2f}'.format(lmbda,fairness_error)
else:
tmp_title = '$\lambda$ = {}, fairness Error = {: .2f}'.format(lmbda,fairness_error)
plt.title(tmp_title)
plt.legend()
plt.tight_layout()
plt.savefig(filename, format='png', dpi = 800, bbox_inches='tight')
plt.show()
plt.close('all')
def plot_fairness_vs_clusterE(cluster_option, savefile, filename, lmbdas, fairness_error_set, min_balance_set, avg_balance_set, E_cluster_set, save = True):
if not osp.exists(savefile) or save == True:
np.savez(savefile, lmbdas = lmbdas, min_balance_set = min_balance_set, avg_balance_set = avg_balance_set, fairness_error = fairness_error_set, E_cluster = E_cluster_set)
else:
data = np.load(savefile)
lmbdas = data['lmbdas']
fairness_error_set = data['fairness_error']
E_cluster_set = data['E_cluster']
# pdb.set_trace()
if cluster_option == 'kmeans':
label_cluster = 'K-means'
elif cluster_option == 'ncut':
label_cluster = 'Ncut'
elif cluster_option == 'kmedian':
label_cluster = 'K-medians'
dataset = (filename.split('_')[-1].split('.'))[0]
title = '{} Dataset ---- Fair {}'.format(dataset,label_cluster)
ylabel1 = 'Fairness error'
ylabel2 = '{} discrete energy'.format(label_cluster)
length = len(lmbdas)
plt.ion()
plt.rcParams.update({'font.size': 13})
fig, ax1 = plt.subplots(dpi=80)
# ax1.set_xlim ([0,length])
ax2 = ax1.twinx()
# ax1.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ax2.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
ax1.plot(lmbdas[:length], fairness_error_set[:length], '--rD' , linewidth=2.5, label = ylabel1)
ax2.plot(lmbdas[:length], E_cluster_set[:length], '--bP' , linewidth=3, label = ylabel2)
ax1.set_xlabel(r'$\lambda$', fontsize=15)
ax1.set_ylabel(ylabel1, color = 'r', fontsize=15)
ax2.set_ylabel(ylabel2,color = 'b', fontsize=15)
# ax2.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
ax1.legend(loc = 'upper right', bbox_to_anchor=(1, 0.6))
ax2.legend(loc = 'upper right', bbox_to_anchor=(1, 0.7))
fig.suptitle(title, fontsize=16)
fig.savefig(filename, format='png', dpi = 800, bbox_inches='tight')
plt.show()
plt.close('all')
def plot_K_vs_clusterE(cluster_option, savefile, filename, K_list, E_cluster_set, E_0_cluster_set, save = True):
if not osp.exists(savefile) or save == True:
np.savez(savefile, K_list = K_list, E_cluster_set = E_cluster_set, E_0_cluster_set = E_0_cluster_set)
else:
data = np.load(savefile)
K_list = data['K_list']
E_cluster_set = data['E_cluster_set']
E_0_cluster_set = data['E_0_cluster_set']
# pdb.set_trace()
if cluster_option == 'kmeans':
label_cluster = 'K-means'
elif cluster_option == 'ncut':
label_cluster = 'Ncut'
elif cluster_option == 'kmedian':
label_cluster = 'K-medians'
dataset = (savefile.split('_')[-1].split('.'))[0]
title = '{} Dataset ---- Fair {}'.format(dataset,label_cluster)
ylabel = '{} discrete energy'.format(label_cluster)
legend_1 = 'Variational Fair {}'.format(label_cluster)
legend_2 = 'Vanilla {}'.format(label_cluster)
plt.figure(1,figsize=(7,5), dpi=80)
plt.rcParams.update({'font.size': 15})
plt.ion()
plt.clf()
plt.ticklabel_format(axis="y", style="sci", scilimits=(0, 0))
plt.plot(K_list, E_cluster_set, '--gD' , linewidth=2.2)
plt.plot(K_list, E_0_cluster_set, '--bP' , linewidth=2.2)
plt.xlabel('Number of clusters (K)')
plt.ylabel(ylabel)
plt.legend([legend_1, legend_2], loc='upper center')
plt.savefig(filename, format='png', dpi = 800, bbox_inches='tight')
plt.show()
plt.close('all')
def plot_balance_vs_clusterE(cluster_option, savefile, filename, lmbdas, fairness_error_set, min_balance_set, avg_balance_set, E_cluster_set, save = True):
if not osp.exists(savefile) or save == True:
np.savez(savefile, lmbdas = lmbdas, fairness_error = fairness_error_set, min_balance_set = min_balance_set, avg_balance_set = avg_balance_set, E_cluster = E_cluster_set)
else:
data = np.load(savefile)
lmbdas = data['lmbdas']
avg_balance_set = data['avg_balance_set']
E_cluster_set = data['E_cluster']
# pdb.set_trace()
if cluster_option == 'kmeans':
label_cluster = 'K-means'
elif cluster_option == 'ncut':
label_cluster = 'Ncut'
elif cluster_option == 'kmedian':
label_cluster = 'K-medians'
dataset = (filename.split('_')[-1].split('.'))[0]
title = '{} Dataset ---- Fair {}'.format(dataset,label_cluster)
ylabel1 = ' Average Balance'
ylabel2 = '{} discrete energy'.format(label_cluster)
length = len(lmbdas)
plt.ion()
fig, ax1 = plt.subplots()
# ax1.set_xlim ([0,length])
ax2 = ax1.twinx()
ax1.plot(lmbdas[:length], avg_balance_set[:length], '--rD' , linewidth=2.5, label = ylabel1)
ax2.plot(lmbdas[:length], E_cluster_set[:length], '--bP' , linewidth=3, label = ylabel2)
ax1.set_xlabel(r'$\lambda$')
ax1.set_ylabel(ylabel1, color = 'r')
ax2.set_ylabel(ylabel2,color = 'b')
ax1.legend(loc = 'upper right', bbox_to_anchor=(1, 0.6))
ax2.legend(loc = 'upper right', bbox_to_anchor=(1, 0.7))
fig.suptitle(title)
fig.savefig(filename, format='png', dpi = 800, bbox_inches='tight')
plt.show()
plt.close('all')
def plot_convergence(cluster_option, filename, E_fair):
# Plot original fair clustering energy
if cluster_option == 'kmeans':
label_cluster = 'K-means'
elif cluster_option == 'ncut':
label_cluster = 'Ncut'
elif cluster_option == 'kmedian':
label_cluster = 'K-medians'
length = len(E_fair)
iter_range = list(range(1,length+1))
plt.figure(1,figsize=(7,5), dpi=80)
plt.rcParams.update({'font.size': 15})
plt.ion()
plt.clf()
ylabel = 'Fair {} objective'.format(label_cluster)
plt.plot(iter_range, E_fair, 'r-' , linewidth=2.2)
plt.xlabel('outer iterations')
plt.ylabel(ylabel)
plt.xlim(1,length)
plt.savefig(filename, format='png', dpi = 800, bbox_inches='tight')
plt.show()
plt.close('all')
if __name__ == '__main__':
cluster_option = 'ncut'
data_dir = 'data/Bank'
dataset = 'Bank'
output_path = 'outputs'
# savefile = osp.join(data_dir,'Fair_{}_K_vs_clusterEdiscrete_{}.npz'.format(cluster_option,dataset))
# filename = osp.join(data_dir,'Fair_{}_K_vs_clusterEdiscrete_{}_3.png'.format(cluster_option,dataset))
# plot_K_vs_clusterE(cluster_option, savefile, filename, [], [], [], save=False)
#
savefile = osp.join(data_dir,'Fair_{}_fairness_vs_clusterEdiscrete_{}.npz'.format(cluster_option,dataset))
filename = osp.join(data_dir,'Fair_{}_fairness_vs_clusterEdiscrete2_{}.png'.format(cluster_option,dataset))
plot_fairness_vs_clusterE(cluster_option, savefile, filename, [], [], [], [], [], save = False)